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import os
import requests
from dotenv import load_dotenv
load_dotenv()
QDRANT_URL = os.getenv("QDRANT_URL")
QDRANT_API_KEY = os.getenv("QDRANT_API_KEY")
COLLECTION_NAME = "physical_ai_textbook"
if not QDRANT_URL or not QDRANT_API_KEY:
raise ValueError("QDRANT_URL and QDRANT_API_KEY must be set in the .env file")
# Ensure URL doesn't end with slash + handle if user put "https://" or not
if not QDRANT_URL.startswith("http"):
QDRANT_URL = f"https://{QDRANT_URL}"
QDRANT_URL = QDRANT_URL.rstrip("/")
HEADERS = {
"api-key": QDRANT_API_KEY,
"Content-Type": "application/json"
}
def init_db():
"""
Initializes the Qdrant collection via REST API.
"""
# Check if collection exists
check_url = f"{QDRANT_URL}/collections/{COLLECTION_NAME}"
response = requests.get(check_url, headers=HEADERS)
if response.status_code == 200:
print(f"Collection {COLLECTION_NAME} already exists.")
else:
print(f"Creating collection: {COLLECTION_NAME}")
# Create collection
create_url = f"{QDRANT_URL}/collections/{COLLECTION_NAME}"
payload = {
"vectors": {
"size": 768,
"distance": "Cosine"
}
}
resp = requests.put(create_url, headers=HEADERS, json=payload)
if resp.status_code == 200:
print("Collection created successfully.")
else:
print(f"Error creating collection: {resp.text}")
def search_points(vector, limit=5):
url = f"{QDRANT_URL}/collections/{COLLECTION_NAME}/points/search"
payload = {
"vector": vector,
"limit": limit,
"with_payload": True
}
response = requests.post(url, headers=HEADERS, json=payload)
if response.status_code == 200:
return response.json().get("result", [])
else:
print(f"Search Error: {response.text}")
return []
def upsert_points(points):
url = f"{QDRANT_URL}/collections/{COLLECTION_NAME}/points?wait=true"
payload = {
"points": points
}
response = requests.put(url, headers=HEADERS, json=payload)
if response.status_code != 200:
print(f"Upsert Error: {response.text}")
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